2

FEASIBILITY ASSESSMENT

FOR A

PROTOTYPE DSS FOR THE ELBE

FINAL REPORT

J.L. de Kok1, H.G. Wind1, H. van Delden1 and M. Verbeek2

with comments by J. Berlekamp3 and M. Matthies3

Enschede,

October 9, 2001

1 Dept. of Civil Engineering Technology and Management, Twente University, Enschede2 INFRAM bv., Zeewolde, The Netherlands.3 Institute of Environmental Systems Research, University of Osnabrück

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1. INTRODUCTION .............................................................................................................................. 41.1 Purpose of the feasibility study...................................................................................................... 41.2 Plan of approach............................................................................................................................. 4

2. PROBLEM DEFINITION.................................................................................................................. 62.1 Introduction.................................................................................................................................... 62.2 Identification of problem owner and problems .............................................................................. 62.3 Management objectives and criteria.............................................................................................. 72.4 Measures ....................................................................................................................................... 72.5 Scenarios ........................................................................................................................................ 8

3. QUALITATIVE DESCRIPTION OF THE DSS ............................................................................... 93.1 Introduction.................................................................................................................................... 93.2 Modular structure of the DSS ...................................................................................................... 103.3 Module 1: landuse change vs. runoff in the Elbe catchment ....................................................... 103.4 Module 2: shipping conditions, flooding, and water quality along the Elbe river ....................... 113.5 Module 3: Hydromorphology and ecology of Elbe river, its banks, and floodplains ................. 123.6 Linking the three modules............................................................................................................ 12

4. INVENTORY OF MODELS AND DATA...................................................................................... 144.1 Introduction.................................................................................................................................. 144.2 Research institutes and research themes ...................................................................................... 144.3 Recommendations for quantitative design ................................................................................... 144.4 Co-ordination with Elbe 2000 program ....................................................................................... 154.5 Discussion .................................................................................................................................... 17

5. RECOMMENDATIONS FOR THE PILOT STUDY...................................................................... 185.1 Scope and purpose of the pilot DSS............................................................................................. 185.2 Structure of the DSS .................................................................................................................... 185.3 Selection of the study area .......................................................................................................... 195.4 Availability of models and data ................................................................................................... 205.5 Concluding remarks ..................................................................................................................... 22

6. REFERENCES .................................................................................................................................. 237. FIGURES........................................................................................................................................... 288. TABLES ............................................................................................................................................ 35

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1. INTRODUCTION

1.1 Purpose of the feasibility study

Over the last decades river basin management has become increasingly complex. Increasingdemands of society regarding use and protection of water bodies, new views and strategiestowards (the making of) policy for river basin management call for a multidisciplinaryapproach for river basin management. Since methodologies and tools for such amultidisciplinary approach are not readily available, the Bundesanstalt für Gewässerkunde(BfG) has initiated the project ‘Towards a generic tool for river basin management’. Theultimate goal of the project is to develop a generic tool which helps the water manager(s) toformulate policy for river basin management and to take appropriate measures to realizepolicy objectives. In November 1999 a feasibility study has started to explore the possibilitiesfor the development of this generic tool as a Decision Support System (DSS). A DSS can bedefined as a computer based instrument that can be used to support the policy making process.In a DSS a structured approach towards river basin management is combined with eminentInformation Technology, leading to an instrument that facilitates the processing, analysis andpresentation of information. A DSS helps the end-user of the DSS to discern whichinformation is relevant at any given time in the policy making process. With this informationthe end-user can enhance the quality of the different actions that are to be taken in the policymaking process. On the one hand these are actions with respect to the contents of the policylike problem analysis, forecasting of future contexts, design and screening of alternatives,impact assessment and comparing and ranking alternatives. On the other hand these involvemore process-like actions like communication, interactive policy making, etc. In order tofocus the feasibility study, use will be made of the data and science for the river Elbe. TheElbe is one of the largest rivers in Central Europe, with a catchment of approximately 148.000km2 which stretches over Germany, Czech Republic, Austria and Poland (see chapter 2 formore details).

1.2 Plan of approach

The feasibility study will result in recommendations for a pilot study focussing on someselected topics, which could be considered for implementation in a period of three yearsfollowing the feasibility study. The feasibility study consists of five phases:

1. problem definition.2. qualitative system design (for the DSS).3. available data and science related to the system design.4. informatics framework for the DSS.5. covering note.

This report focuses on phase 2 and 3, for the other phases separate reports will be written. Thefeasibility study is carried out by order of the BfG by the University of Osnabrück, theResearch Institute for Knowledge Systems, the University of Twente, and Infram.

The main function of the DSS will be to link selected management measures and scenariossuch as dike shifting and agricultural development to the objectives identified during theproblem definition study. For the envisaged Elbe-DSS results could be presented in the form

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of flood level tables, habitat maps, graphs for economic development etc. The plan ofapproach for the feasibility study encompassed [1]:

• an inventory of problems to be included in the DSS, management objectives andmeasures to deal with these problems; this point is discussed in chapter 2 (problemdefinition) of this report

• a qualitative system description of the prototype DSS; discussed in chapter 3

• an inventory of the models and data available in Elbe Ecology and Elbe 2000 programs;discussed in chapter 4

• an inventory of the models and data that can be incorporated in the prototype model tolink the measures to the objectives; discussed in chapters 3 and 4

As for the design of the DSS the following steps can be distinguished [2, 3]:

a. problem definition

b. qualitative system description;

c. quantitative system description;

d. implementation of DSS;

e. validation;

This report is organized as follows. The problem definition forms the subject of a separatereport, but is briefly summarized in chapter 2 because it formed the starting point for thequalitative system description. The qualitative design is discussed in chapter 3. In order todeal with the differences in spatial and temporal model scales the suggestion of a modularstructure is made for the prototype. In view of the quantitative design phase it should bemade clear where models and data are available or being developed. An inventory of themodels and data that are already available or will be developed in the next two years wasmade on the basis of modeling questionnaires that were submitted to the institutes. The mainresults of the answered questionnaires are presented in chapter 4. The report is concluded witha number of issues that need further discussion, as well as some concluding remarks on thefeasibility of the prototype. Selected references, provided in response to the questionnaires,tables, and figures can be found at the end of the report.

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2. PROBLEM DEFINITION

2.1 Introduction

As explained in the introduction section the problem definition forms the starting point for thedesign of a DSS. The purpose of this phase is to identify the so-called end-users of the model(i.e. persons or institutes that can be considered as problem owner), make an inventory ofrelevant problems, determine the objectives to be achieved, identify tentative measures, anddetermine the spatial, temporal, economic and other boundaries of the system. In short: theproblem definition delineates the scope of the study. For details on the results of the problemdefinition study for the Elbe, which was carried out by M. Verbeek (Infram bv) and H. vanDelden (Twente University), we refer to the companion report. What follows is a briefsummary.

2.2 Identification of problem owner and problems

It was not possible to identify a single problem owner for the Elbe catchment. Instead anumber of potential decision-making institutes, each having their own objectives andmeasures, were identified:

• Bundesanstalt für Gewässerkunde (BfG)• Arbeitsgemeinschaft zur Reinhaltung der Elbe (ARGE-Elbe)• Internationale Kommission zum Schutz der Elbe (IKSE)• Wasser- und Schiffsverwaltung (WSV)• Bundesanstalt für Wasserbau (BAW)• Länder in the catchment area• Biosphärenreservate• Bundesministerium für Verkehr, Bau- und Wohnungswesen (BMVBW)• Bundesministerium für Ernährung, Landwirtschaft und Forsten (BMELF)• Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit (BMU)• Wasserwirtschaft

A selection of end-users will be made in view of the consequences for the design of the pilot.

The principal function of the prototype is to demonstrate how problems can be dealt with. Bydefinition a problem is a difference between the actual and desired state of the system [37].Without a problem there is no need to analyze measures. It is only possible to identify theproblems to be addressed by the prototype model once a definitive choice for a specificproblem owner has been made. Nevertheless, a number of problems were mentioned byrepresentatives of several of the institutes listed above, as well as researchers involved in theElbe Ecology and Elbe 2000 program. The problems that could be worth including in theprototype model are:

• how to improve the socio-economic use of the river basin (shipping, tourism, fisheries,agriculture, etc)

• how to provide a sustainable level of flood protection

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• how to reach a sustainable improvement of the physical, chemical, and biological stateof the Elbe river and its tributaries

• how to increase the ecological value of the river and the floodplains in the Elbe riverbasin

2.3 Management objectives and criteria

A management objective is a desired state of the system that the decision makers want toachieve. The objectives are closely related to the problems. For example, if river navigation isa problem, the objective can be to make a particular section of the Elbe river navigable. Or, ifthe chemical state of the river turns out to be a problem, the reduction of the concentration ofparticular pollutants can be an objective. Achievement of the objectives is measured by meansof (usually quantitative) criteria such as a guaranteed water depth of 1.60 m. For the design ofthe DSS the objectives are of particular importance as they determine which information themodel should provide to its users. Referring to the identified problems the followingobjectives can be discerned:

• improvement of social-economic useimprovement of the navigability of the Elbe rivermaintenance/improvement of the agricultural yielddevelopment of tourism and recreationimprovement of the conditions for fisheries

• flood protectionreduction of risk of flooding

• improvement of physical, chemical and biological state of the Elbe and its tributaries andincrease of the ecological value of the river and its floodplains

river and ground water qualitysoil quality of the river bed and the floodplainsimprovement of the ecological functions of river and its banksimprovement of the ecological functions of floodplainsimprovement of the ecological functions of the catchment area

For a full inventory of objectives and a list of the criteria corresponding to the objectivesstated above we refer to the problem definition report.

2.4 Measures

As stated before the main function of the DSS is to link the measures that can be implementedto solve the problems to the objectives. Suggestions for promising measures can be made bythe end-users themselves, or the team of researchers designing the model. Although themeasures are tentative their selection should be made with care. There is no point in analyzingmeasures that are too expensive or unacceptable for other reasons. Furthermore, one should beaware of the models and data that are needed to analyze the consequences of the selected

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measures. For the prototype model it is recommended to select a limited number of measures.These measures address the following four themes:

1. High water management: dike shifting and other measures:• dike shifting (space for the river);• reduction of buildings and other treasuries in the foreland;• provide information on high water management.2. Water quality:• reduction of point- and non-point-source pollution by improving agricultural practice

(Nährstoffe, Pflanzenschutzmittel);• reduction of pollution by hazardous substances (Schadstoffe);• improving/building waste-water treatment plants.3. Groyne modification:• groyne modification.4. Reduction of erosion:• adding material to the river bed/sand suppletion.

Tourism is also mentioned as an important issue for the future. However, no specificobjectives and measures could be identified at this time. It is therefore recommended toconduct a study on the expected and/or desired developments of tourism along the Elbe in thefuture. When possible objectives and measures have become more clear, incorporation oftourism in the prototype model could be considered.

2.5 Scenarios

Scenarios or future context in the terminology of Miser and Quade (Miser and Quade, 1985)are uncertain physical, social-economic, and other conditions that may affect the system understudy, but are beyond control of the decision makers. This means that the scenarios providethe exogenous input for the model system. Although scenarios can be considered as a specialclass of models there is one major distinction with the models in the system itself is that is itsusually not meaningful to indicate which scenario is most plausible. For the prototypescenarios can be introduced for:

• economic conditions;• demographic conditions;• land use change in the catchment;• hydrological conditions;• climate change;• the Czech input of pollutants into the Elbe river.

The final selection of relevant scenarios depends strongly on the system design.

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3. QUALITATIVE DESCRIPTION OF THE DSS

3.1 Introduction

The purpose of the qualitative design is to provide a conceptual framework for the linkage ofmanagement measures to the objectives. At the highest level of abstraction (Figure 1) thedistinction can be made between social-economic functions, natural functions, and physicalprocesses. Decision makers can intervene by introducing measures that affect either one ofthese subsystems, or the interactions between them. A more detailed schematization for theElbe system is given in Figure 2. Here we find the elements mentioned in section 2:

scenarios to describe exogenous factors beyond control of the decision makers but influencingthe system, examples: climate change, population growth, the Czech input of pollutants. Theadvantage of using scenarios is that the impacts of certain processes can be accounted for,even if these are not part of the system.

the measures identified during the problem definition such as groyne shape modification, dikeshifting, and sand suppletion.

the social-economic functions economic activities taking place in the Elbe catchment, forexample agriculture, tourism, and shipping.

the natural functions found in the catchment, the river itself and its banks, and the floodplains.Examples: macrozoobenthos and floodplain forests

physical processes which can influence both the natural functions (morphological changes,water quality) and social-economic functions (flooding risk, drinking water). For the Elbe2000 program these concern concentrations of suspended matter and sediment. For the ElbeEcology program this concerns hydrological, hydraulic and morphological processes.

management objectives which form the output of the system, such as agricultural productionvalue or the navigation depth in a certain section of the Elbe river. Achievement of theobjectives is measured by comparison of the state of the natural and user functions accordingto certain criteria.

For clarity the interactions between scenarios, functions, measures, and objectives are notshown in the diagram. Instead each element is referred to by a two-digit code. For example,14-31 is the impact of dike shifting on the floodplain ecology.

The qualitative diagram of Figure 2 is based on the tentative results of the problem definitionstudy. This ensures that only feasible management measures and relevant objectives areincluded. In some cases the linkage between different elements of the system will not be partof the research themes addressed in the Elbe Ecology and Elbe 2000 program. For practicalreasons, and in view of the example function of the prototype, a selection must be made ofkey functions and processes, which are sufficient to demonstrate the added value of anintegrated model. Although the diagram is designed for the Elbe system, the approachfollowed is generic and can be applied to river-basin management in general.

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3.2 Modular structure of the DSS

One of the problems faced in the design of the prototype is that models and data are beingcollected at a variety of different spatial and temporal scales, ranging from 1000 km2

subcatchments for the landuse to 1 m grids for habitat modeling in floodplains. In the formercase data are collected for the complete catchment, whereas in the latter case the study sitesare limited to a few km2. The question is then how these different models and data can beincorporated in a common framework of analysis. Figure 3 shows how the problem could beapproached in the prototype DSS. A distinction is made between three levels of analysis,which exist in the research projects of the Elbe Ecology Program. At the highest level ofanalysis (gray box Catchment Module) we find the processes, which are being studied at thescale of the complete Elbe catchment of 150.000 km2. Here we find the models describing theimpact of landuse and hydrology on diffuse (nutrient) runoff as well as the impact of pointdischarges. At this scale level the time horizon is long (25-100 years), and the spatial andtemporal resolution low (100-1000 km2 and time steps of months or years).At the second level of analysis (gray box labeled River Module) we find the modelspertaining to the Elbe river of 700-800 km in length. This includes, for example, modelsdescribing the navigation condition, flood risk, and water quality. Although a variety ofmodels can be used for these purposes a one-dimensional model would be more appropriatefor the prototype DSS. For the river module the spatial and temporal detail will be in the orderof 100 m-10 km, and weeks to years, depending on the type of processes studied (bed-levelchanges will require less temporal resolution than flood-level predictions).At the third level of analysis (River Section Module) we find the most detailed models thatdescribe the impacts of river engineering measures such as dike shifting and the habitatconditions for different species in the river, its banks, and the floodplains. At this scale thelevel of spatial and temporal detail will be in the order of 10-50 m. This module could bedeveloped for a well-chosen example section of the Elbe river of 10-100 km, which wouldbe representative for the Elbe river in general, and for which the data and models are availableor can be collected in the time frame of the pilot study. Preferably the three modules shouldbe linked top-down as shown in Figure 3, by selecting output variables of the higher levelmodules that form input variables for the lower-level modules. For example, the total nitrogenload calculated for the catchment can be used as input for a 1D water quality module for theElbe river. In turn the water levels or flooding frequencies calculated in the river module canbe used in the ecological habitat models at the third level of analysis. A general discussion ofthe functions of the three modules will be given in the following sections. More detailedinformation on the available models and data can be found in chapter 4. What follows is asummary of the research questions that are to be answered for the design of the three modules.Naturally, many of these questions formed or form the subject of research conducted in theframework of the Elbe Ecology and the Elbe 2000 program. For the feasibility assessment itshould be examined to what extent the models and data can be made available to answer thesequestions.

3.3 Module 1: landuse change vs. runoff in the Elbe catchment

The purpose of Module 1 is to describe the impact of scenarios for landuse and climatechange on the total load into the Elbe of selected substances (P, N, sediment) from the Elbecatchment. Furthermore, for the integrated DSS information is desirable on the economicaspect of landuse change. Landuse change is primarily a matter of in-/extensification andchanging agrotechnological practice rather than the transition of one type of landuse to

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another (priv. comm. dr. Behrendt, IGB Berlin). Module 1 also includes the impact ofindustrial and urban activities with direct discharge of waste water. Here also the type ofwaste water treatment has to be taken into account. The design of Module 1 encompasses thefollowing research questions:

1a. for which pollutant substances should the total load be estimated?1b. which changes in landuse and agricultural practice influence the load of these

substances per unit area and how?1c. what are the social-economic consequences of these changes?1d. which physical conditions affect the pollutant load and how?1e. how is the load per area distributed into the Elbe river?

At the present moment a number of models have already been completed that can handlethese issues, mainly at PIK and IGB (Table 1). The question is whether these are suitable forthe DSS. Very complex models that demand long computing times are not desirable, butcould perhaps be aggregated before application. This will be further discussed in chapter 4.

3.4 Module 2: shipping conditions, flooding, and water quality along the Elbe river

The purpose of Module 2 is to provide insight in the conditions for shipping (water level,velocity, and bed level), water quality (selected parameters), and flooding frequency (in viewof the ecological functions of Module 3) and flooding risk (in view of the economicobjectives) along the German part of the Elbe river. In the model a 1D hydromorphologicalmodule plays a central role, which means that the data must be available. At present such amodel is available for the Elbe, namely at the University of Karlsruhe. Water quality is a keyaspect of Module 2, affecting economic functions (fishing, drinking water supply) as well asecological functions along the Elbe river. Although the last decade showed considerableimprovement of the river water quality in the Elbe river itself, there still are problems withpollutants such as heavy metals and fertilizers. Despite improved waste-water treatment afterthe unification diffuse agricultural discharge and residual waste remain problematic.Therefore, the linkage of Module 1 to Module 2 (land use) is important. In this sense, waterquality forms the basis for linking the results of the Elbe 2000 program with those of ElbeEcology in the DSS.

The design of this module encompasses the following (related) research questions:

2a. which physical conditions affect the water level, bed level, and stream velocity andhow?

2b. which physical and social-economic conditions affect the flooding frequency andflood risk, and how?

2c. how does the future water quality of the Elbe river depend on these physical andsocial-economic conditions?

2d. what is the impact of land-use change (Module 1) on the river water quality ?

River engineering measures such as groyne modification and dike shifting affect the hydraulicconditions and are of importance as well for the first two questions. The global impacts of

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these measures can be addressed in Module 2, whereas the local consequences can be studiedin more detail in Module 3.

3.5 Module 3: Hydromorphology and ecology of Elbe river, its banks, and floodplains

The purpose of Module 3 is to demonstrate at a more suitable scale the impact of changinghabitat conditions such as the groundwater level or flooding frequency, and two example riverengineering measures (groyne modification and dike shifting) on the state of key ecologicalfunctions of the river itself, the banks and floodplains. In this respect suitable means with asufficient level of spatial-temporal detail, and for a selected and representative section of theriver. Furthermore, erosion of the river bed could be included as a separate topic. Severeerosion problems exist only in the river section near Torgau (priv. comm. BAW Karlsruhe).Its relevance has been mentioned in view of the conditions for shipping (due to the increasedwater depth and velocities) and the potential lowering of the groundwater level (which mayaffect ecological functions). However, the significance of the latter problem is not clear at themoment (it could be the subject of a preliminary sensitivity analysis prior to the formulationof a definitive model for the prototype). A river engineering measure corresponding to theerosion problem is sand suppletion (Figure 3).

Summarizing, the design of Module 3 encompasses the following research problems:

3a. which section of the Elbe river and its floodplains is most appropriate for Module 3?3b. which are the key ecological functions in the Elbe river, banks and the floodplains,

and how can the state of these functions be measured3c. which habitat factors are of most influence on these ecological functions?3d. which factors influence bed erosion and how?3e. how does bed erosion affect the groundwater level?3f. how are these habitat factors affected by the selected river engineering measures?

Incorporation of river- and groundwater quality in Module 3 should depend on its significanceas a habitat condition (a general water quality model is already available in Module 2).

3.6 Linking the three modules

For conceptual reasons the three modules should not function independently, but must belinked in some way. Integration can take place in two ways. Top-down integration is the moreobvious approach and means that processes at a higher scale will influence the system at alower scale. For example the water level calculated in Module 2 for a particular section of theriver can affect ecological functions at the local level described in Module 3. This could implythat the flooding frequency in all the cells in the floodplain along the section in questiondepends on one value of the water level in the river, as well as the elevation of each cell.Sometimes the top-down integration will have consequences for the type of models needed.The incorporation of water quality in Module 2, for example, requires a pathway model forpollutant transport in Module 1.A different approach is to integrate the three modules in a bottom-up way. An example ofbottom-up integration is the influence of molecular diffusion on large-scale transportprocesses. For the Elbe DSS bottom-up integration could mean that local processes have an

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impact on a meso (river) and even macro (catchment) scale of analysis. The question howeveris, whether the processes included in the DSS for the Elbe (such as the habitat models) have abottom-up influence.

At least these two interactions between the modules can be discerned:

• pollutant load of Module 1 as input for water quality model in Module 2

• discharge, water quality, and water level calculated in Module 2 as input for the habitatmodels in Module 3

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4. INVENTORY OF MODELS AND DATA

4.1 Introduction

The qualitative system design presented in chapter 3 does not yet ensure the feasibility of theDSS. It will be necessary to determine whether the models and data that will be available aresufficient for the implementation of the DSS. This means that the research questionsformulated in sections 3.3-3.5 can be answered at the appropriate scale on the basis of thecompleted and ongoing research programs. Gaps in the research program should be identifiedas early as possible. A questionnaire was therefore distributed among the participatinginstitutes, which resulted in an inventory of models and data, and a number of characteristicswhich are relevant for the design of the integrated system, such as the scale of the study area,the degree of spatio-temporal accuracy, development stage, key input and output variables,and the degree of genericity or case-specificity. Table 1 summarizes the main results obtainedfrom the questionnaires.

4.2 Research institutes and research themes

Table 1 indicates what models and data are available at the moment, or will be developed, ingeneral. The time step of the models and data ranges between only 10 seconds to 5 years ormore, while the spatial resolution lies between 1 m and 5-10 km. It is clear that for the DSScomplete integration of models and data showing such differences, even if it were possible,would not lead to meaningful results. The modular structure proposed in the previous chaptercould ensure an acceptable degree of consistency of scales within the modules.In principal the selection of scales should be based on the aim of the model in question andthe desired degree of accuracy. For example, in Module 1 the aim is to describe the impact oflanduse change on the runoff. If the influence of diffuse agricultural sources on the runoff ofnitrogen can be estimated with a spatial resolution of 10 km it will not be necessary to usefiner resolutions. Clearly the difficulty is that the scale issue can only be dealt with by takinginto account the function of the different models for the integrated systems in view of theconsistency of the DSS. The obvious approach is to make tentative choices for the scales inthe prototype. Once the DSS has been completed it can be used to conduct sensitivity analysesand uncertainty analyses, to identify which submodels are too detailed or too coarse.The principal question for the feasibility of the quantitative design is whether the researchcarried out by the institutes pertains to system variables corresponding to a consistentqualitative network (such as the example of Figure 3). In addition to the information providedin Table 1 a summary of key system variables and institutes studying the relationshipsbetween these variables, is given in Tables 2a-2c.

4.3 Recommendations for quantitative design

Regarding the modeling of processes that are to be incorporated in the DSS (Figure 3) thefollowing situations can be discerned:

• processes for which data and models are available at the appropriate spatio-temporalscale; these can be incorporated in the DSS. A number of processes are studied at more

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than one institute, in that case one could coordinate the effort. Example: 1D flood levelmodel for the Elbe river.

• processes for which appropriate models and data are available, but not for the study areain mind (this will primarily occur for the processes of Module 3). In this case one candetermine whether the existing models and data can be transferred to the selected studyarea. Example: fish habitat model.

• processes for which models and data are available, but not at the appropriate level ofspatial-temporal detail (usually the level of detail exceeds the requirements for the DSS).In this case one should determine whether the models can be upscaled. Example: beetlehabitat model. If upscaling is not desirable or impossible, the model can be given a“dummy” function, for example by referring to a hyperlink in the DSS.

• processes for which models and data are available at the appropriate scale, but in a formthat cannot be directly applied in the prototype DSS. For example, a physical scale model(gegenständliches Modell) for river flow. In that case the model must be translated into asuitable form first, for example an analytical approximation.

• processes for which models and data are not immediately available. In that case oneshould formulate a new model and collect the necessary data (this should be possible inthe time span of the prototype development) at the appropriate scale. Example: influenceof bed erosion on groundwater level.

Finally, a number of research themes can be identified in the diagram of Figure 3 that havenot been addressed in the Elbe Ecology program. These include:

• Large-scale (non-local) models that can describe the influence of aggregated habitatfactors which are available at the river scale, such as the flooding frequency andinundation depth, on ecological functions. These types of models could facilitate theintegration of Module 2 with the ecological state indicators. Example: ecotype approach[38].

• A model describing the impact of river bed erosion on the groundwater level in thefloodplains. At the moment it is not clear whether this interaction is significant, but itcould be important for Module 3 and perhaps Module 2.

• If the influence of water quality on ecological functions is decided to be significant (so farthis interaction has only been mentioned in relation with consumption fish) models for therelationship between chemical parameters and ecological quality are needed.

• Economic models for the valuation of flood risk (damage times probability) and theimpact of hydromorphological changes on shipping.

4.4 Co-ordination with Elbe 2000 program

The Elbe 2000 research program was initiated by the Federal Ministry of education, science,research and technology (Bundesministerium für Bildung, Wissenschaft, Forschung und

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Technologie) in 1991. The main objective of the program was an complete inventory of thepollutant situation (mainly heavy metals) after the German reunification aiming to aremediation of the Elbe and the Elbe catchment. The program was divided in 3 phases - 2focused on the Elbe tributaries and one on the Elbe itself. Table 3a gives an overview of thegroups of measured parameters ordered by each river (see also Figure 4a). Summarized theresults from the Elbe 2000 program gave a good inventory of the main pollutants (especiallyheavy metals) in the water, suspended matter and sediment of the Elbe and its tributaries.Because of the time frame of the sampling from 1991-1996 this is an inventory of the pastsituation. Nevertheless the results may be very useful for calibrating and testing the waterquality model.

The ARGE Elbe and IKSE run a continuous measuring program in the Elbe with a hugespectrum of parameters measured (see Table 3b for parameters of the ARGE program andFigure 4b for the location of measuring sites). These data will be very useful for validating thewater quality model.

Both, the data from Elbe 2000 and the continuous monitoring by the ARGE /IKSE will beavailable via the new ELBIS information system, which will be launched in Summer 2000.As it is described in the IT-Framework Report in detail it is planned to integrate a dataconnections to ELBIS into the DSS.

The main problem for integrating the data from Elbe 2000 into a model system for waterquality management will the missing locations of most of the sources of emitted pollutants.As far as can be seen up to now only scarce georeferenced emission data (locations ofindustry, waste water treatment plants, residual wastes or other) are available from the Elbe2000 program and from the IKSE (IKSE, 1998). For a complete data base concerning themodeling of river water quality the following data are missing:

- Locations and discharges from all urban waste water treatment plants(discharges may be calculated from demography and market data)

- Locations and discharges from industrial sites (discharges may be calculated byproduction data eventually)

Once suitable water quality indicators have been selected on the basis of their impact on theecological functions, or meaning as general indicators for pollution, a 1D water quality modelcan be formulated for the Elbe river. This model should account for the physical, chemical,and biological factors that affect the spatial and temporal patterns in the concentration of theselected substances. The European Waters Framework Directive could serve as a guideline toidentify key substances as water quality indicators. The relevant research questions forincorporating water quality in the prototype model are:

• what substances are representative chemical indicators? A possible choice could include(priv. comm. dr. Berlekamp): a detergent, nitrate, a pesticide, a heavy metal (e.g. Hg), anda solvent (e.g. benzene)

• what substances must be included in view of their impact on ecological functions, andhow can this impact be described?

• how is concentration of these substances in the Elbe river related to the source discharges,treatment facilities, and physical processes in the catchment?

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4.5 Discussion

The feasibility study pointed to a number of methodological issues that need to be clarifiedbefore initiating the design of the prototype DSS:

• is the list of formulated research questions (sections 3.3 - 3.5) complete?• in which ways can the three modules be integrated?• which representative section of the river could be selected for Module 3?• which habitat factors and ecological indicators are suitable?• how can the research gaps mentioned in section 4.3 be solved?• to what extent are models and data site-specific?• which role will water quality models play in the DSS?

Besides methodological issues there are organizational questions that need to be answered aswell if the decision is taken to develop the prototype model:

• how can research not directly applied in the DSS be presented?• how can the effort of institutes working on similar research themes be combined?• how can the end users be kept involved during the design of the prototype?

Regarding the objectives for the feasibility study (section 1.2) the following can be noted. Theproblem definition resulted in the identification of a number of potential end-users of theprototype model, relevant problems, management objectives and feasible measures. Theseformed the starting point for the qualitative system description. The classification made insystem diagram of Figure 2 is based on the completed and planned research in the ElbeEcology program. In order to overcome the problem of scale differences a modular structurehas been proposed for the pilot model. An example of such a system is given in Figure 3. Thesuggestion there is to distinguish between models pertaining to processes at catchment, river(e.g. 1D) and local level (e.g. a representative section of the river, its banks, and floodplains).Depending on the module, ecological, hydromorphological and social-economic processescan be described. For each of the three modules one can identify a number of researchquestions that are relevant for the quantitative design of the prototype (sections 3.3-3.5), andthat are being addressed by one or more of the institutes participating in the Elbe Ecologyprogram. The diagram of Figure 3 indicates which selection of research institutes cancontribute the models and data needed to describe each interaction. Table 1 lists the maincharacteristics of these models and data using the answered questionnaires as source ofinformation (the list requires some completion so the mentioned researchers are invited tocontribute). A minimal set of system variables for the three modules is given in Tables 2a-2c.

The final chapter consists of a number of recommendations for a feasible design of the pilotstudy. These recommendations regarding the feasibility of the pilot study should address thescope of the DSS, the selection of an appropriate study area, the structure of the informaticsframework, and the availability of models and data to implement this structure.

18

5. RECOMMENDATIONS FOR THE PILOT STUDY

5.1 Scope and purpose of the pilot DSS

The aim of the pilot study is to develop a trial version of a decision-support system (DSS) forintegrated management of the Elbe. The models and data that have been collected in theframework of the Elbe Ecology and Elbe 2000 research programs should form the basis forthis DSS. The main functions of the DSS are:

• analysis of management alternatives• communication among scientists and decision makers• management function• library function

The design should be open and sufficiently flexible to allow for the incorporation of morecomplex models if necessary. Furthermore, the pilot DSS should be generic and applicable forthe management of other river basins in Germany and abroad.

5.2 Structure of the DSS

In principle the modular design of the system presented in chapter three (Figure 3), canaccommodate all the relevant ecological, physical, and economic variables. The pilot DSSshould encompass four layers:

1. a spatial overview model of the Elbe catchment (Figure 4c), which fulfills a libraryfunction providing read-only access to the models and data collected in Elbe2000 andElbe Ecology (library function)2. a system network of executable 1D process models for an appropriate section of theriver, which fulfills an analysis and communication function3. read-only access to a selection of 2D process models (library function)4. read-only access to a selection of 3D process models (library function)

It is convenient to refine the diagram of Figure 3 to the level of system variables. Figure 5schematizes the second layer of the DSS in this way. The network of interacting 1D processmodels is based on discussions with scientists at the Institute of hydraulic research atKarlsruhe University, and the Bundesanstalt für Gewässerkunde at Koblenz, which took placeon July 17-19, 2000. The design is restricted to four management objective categories:navigability, water quality, flood risk, and ecological quality. The interaction betweenmanagement measures, system elements, and the management objectives are denoted byindices a-s. For a number of processes alternative models and data will be available, whichdiffer in the level of detail, data requirements and computing time. This is no problem as thedesign of Figure 5 allows for refinements from one-dimensional process models to two-dimensional, or even three-dimensional models (which are only included qualitatively in thepilot model).

19

Summarizing, with respect to the design of the pilot DSS one can distinguish threeapproaches:

A. pilot system based on a selection of 1D models and data available in Elbe Ecology andElbe 2000B. simplified pilot system based on mostly 1D models available at the BfGC. extended system based on advanced 2D and 3D models such as WAQUA and Sobek

The initial aim is to start with design option A. If problems are encountered with respect tothe formulation and application of models for some processes one can resort to option B. In alater stage the design can be revised to incorporate advanced option C models.

5.3 Selection of the study area

In principle the DSS should support decision making along the whole Elbe river. However, asmodels and data are available only for restricted sections of the Elbe, the pilot DSS should bedeveloped for a selected section of the river. Preferably, existing models will be recalibratedfor this river section. The choice of the study area should be based on the following criteria:the existence of a real problem, the fact that management measures are in effect or planned,the fact that changes due to these measures are expected to be significant, and the readilyavailability of models and data from the Elbe Ecology and/or Elbe 2000 programs. Preferablythe case study should involve a combination of ecological, hydraulic, and economic factors.

The following list is a selection of management problems (or objectives) and potentialsolutions for the Elbe:

• flooding safety (measure: dike displacement)• nature protection (measure: protected area)• navigability (measure: groyne modification)• water quality (measure: effluent treatment)• bed erosion (measure: suppletion)

Table 3 shows where these problems and measures are situated. The main problem areas are:

1. the 100 km erosion stretch between Magdeburg and Torgau (bed erosion and shippingproblems) with suppletion as management alternative [Elbe KM 121-235]

2. the nature reserve Mittlere Elbe (nature protection) [Elbe KM 222-302]3. the dike displacement stretch Wittenberge-Lenzen (flooding problems) [Elbe KM 438-

495]

In addition, Table 4 shows where models and data are available. Combining Table 3 andTable 4, and taking the integrated point of view into consideration (simultaneous role forhydraulics, economics, and ecology) the Elbe section between Tangermünde and UntereHavel (KM 400-425) is a good choice for the pilot study. In this area hydraulic andgroundwater models will be available, an ecological study is conducted in the framework ofthe RIVA program, and there are two dike shifting areas between KM 412 and KM 421 (nearSandau). Moreover, the impacts of groyne shape modification are studied here (TUDarmstadt).

20

5.4 Availability of models and data

The system structure shown in Figure 5 points to the need for models pertaining to fivedifferent fields of expertise:

• flow channel hydraulics and morphology• groundwater dynamics• river water quality• flood risk• floodplain ecology

The models and data required for design strategy A have been discussed in chapter 4 and aresummarized in Table 1. Because many subprograms are in an initial or ongoing phase aselection must be made of the themes listed in Table 1. Restrictions can be made on the basisof the design of Figure 5 and the readily availability of models and data. Table 5 summarizesthe models and data available at the moment for the simplified pilot design of the DSS (optionB).

Four models available at the BfG can provide the framework needed for design option B.These are: HBV [42, 47] for rainfall-runoff, KWERT [40] for hydromorphology, QSIM [41,46] for water quality, and INFORM [43] for the ecological impacts. In addition to these fourmodels the design of Figure 5 requires a groundwater model, an economic model (forshipping and flooding), and a Digital Terrain Model.

HBV

The HBV model [42, 47] has been developed in Sweden. A PC based version of the model,SMHI (1996) is in use by the BfG. Subroutines for snow accumulation and melt, soil moistureand runoff are included. The model is semi-distributed and uses subbasins as hydrologicalunits. In each subbasin land is classified as being forest, open land or lakes, and an elevationdistribution is given. A daily time step is used for the standard runs of the SMHI version ofthe model. The soil moisture module uses potential evapotranspiration and provides the inputfor the runoff module.

Model input :

• daily precipitation• monthly evapotranspiration• land use data• runoff data (for calibration)

KWERT

KWERT [40] has been developed at BfG and can be used to calculate water levels and manyother variables (roughness, average flow velocity etc) under conditions of stationarydischarge. The model is based on a solution of the Saint-Venant equation for stationarydischarge. The friction term is obtained by solving the Manning-Strickler equation. The

21

equations are solved for layered cross profiles for each river section, which can be dividedinto three sub elements (main channel, banks, and floodplains).

Required input data:

• water level at end of section• resistance values• cross section geometry

QSIM

QSIM [41, 46] can be used to model water quality and plankton dynamics in rivers [46]. Themodel has a modular structure and includes hydraulic, meteorological, water quality, andtransport modules. QSIM can deal with stationary as well as non-stationary dischargesituations. Examples of predicted variables are DO, suspended sediment, phyto- andzooplankton, pH, nitrogen and phosphorus. First-order differential equations are used for thispurpose.

Required input data:

• river geometry• discharge• various climatological data• biochemical water quality parameters: BOD, COD, N, Chl-a

INFORM

The INFORM [43] (INtegrated FlOodplain Response Model) model uses canonicalconcordance analysis (CCA) to determine the linkage between abiotic conditions andecological indicator variables. A habitat suitability curve plays a central role in the INFORMmodel. The data requirements encompass at least (over 60 parameters are sampled):

• groundwater head• flooding frequency• flooding duration• digital terrain model

groundwater dynamics

Several 2D groundwater models based on a finite element approach are available. TheINFORM model is driven by Visual Modflow [43], using the channel water level and the soiltransmissivity as inputs. A groundwater model is also available at IWW Karlsruhe [48] withsimilar requirements. Both models have been calibrated for specific sites (Table 4), but can beapplied elsewhere along the Elbe if calibration data are available. In case the required data aredifficult to obtain one can resort to a simpler approach. Examples are a 1D approach based onthe Boussinesq equation [49] or an approach were the groundwater level is assumed to be

22

equal to water level in the river channel (a valid approximation after an extended period ofdrought).

economic model

Economic aspects of the pilot system include shipping revenues (as a function of transportablefreight) and the damage incurred by floods. A model to relate the water level to the potentialfreight can be obtained from PAWN study [50] for example. Damage functions relating flooddamage for different land-use types to the maximum water level are available from theBundesamt für Gewässerkunde at Munich.

digital terrain model

Land-use cover and elevation are inputs required for the calculation of flood damage andecological impacts in the floodplains. For this purpose digital terrain models are availablefrom the BfG for some of the nature reserves and from IWW Karlsruhe for the Elbe river as awhole.

In summary, it can be concluded from Table 4 that most of the data required for these modelsare available in the Elbe Ecology and Elbe 2000 program, with the exception of functions forshipping freight and flood damage. A number of models such as HBV, KWERT and QSIMwill need recalibration for the study area.

5.5 Concluding remarks

The objective of the feasibility assessment is to determine whether a generic prototype toolfor integrated river-basin management can be developed, with the Elbe catchment asapplication area. This means that a. management problems and potential measures to abatethese problems have to exist, b. an appropriate study area is required, and that models anddata should be readily available to implement the prototype. Interviews were held withdecision makers and modeling questionnaires sent to the researchers involved in the ElbeEcology and Elbe 2000 programs. This resulted in the suggestion of a modular design (Figure3) for the prototype, comprising submodules for the Elbe catchment, river channel, and localimpacts on ecology. More specifically, the pilot system should be composed of differentlayers, which have the function of analyzing management alternatives, or allow read-onlyaccess to more detailed models that are not yet included in the system (library function). Anappropriate study area was identified on the basis of the presence of a management problembearing ecological, hydraulic, and/or economic aspects, the fact that management measuresare planned in the near future, and the availability of models and data from the Elbe 2000 andElbe Ecology research programs. The recommendation is to develop the pilot DSS for theregion between Elbe Km 400 and Km 425 (Figure 4c). In the preceding sections the modulardesign of Figure 3 has been refined to a number of key objectives (Figure 5). Three differentstrategies can be discerned with respect to the design of the pilot system (section 5.2): a pilotsystem (option A), a simplified pilot system (option B), and an extended model (option C).The general conclusion is that the design of option A is probably feasible, while that of optionB is certainly feasible.

23

6. REFERENCES

[1] Workplan for the feasibility study for the Elbe DSS, December 1999, Bundesanstalt fürGewässerkunde, Koblenz.

[2] Miser Hugh J. and Quade Edward S., Handbook of systems analysis: overview of uses,procedures, and applications, and practice, John Wiley and Sons, Chichester, 1985.

[3] J.L. de Kok and H.G. Wind, Methodology for sustainable coastal zone management in thetropics, Final report prepared for the the netherlands organization for the advancement oftropical research (WOTRO) under grant WK.79.35, 272 p., March 1999.

[4] ECETOC, Speical Report No. 16, GREAT-ER User Manual, European Centre forEcotoxicology and Toxicology of Chemicals, Brussels, March 1999.

[5] Kurzinformationen zu Forschungsvorhaben, Ökologische Forschung in derStromlandschaft Elbe (Elbe-Ökologie), Projektgruppe Elbe-Ökologie in der Bundesanstalt fürGewässerkunde, Berlin, November, 1999.

[6] Fachtagung Elbe, Dynamik und Interaktion von Fluss und Aue, 4. bis 7. Mai 1999,Wittenberge, Universität Karlsruhe.

[7] R. Kunkel und F. Wendland, Der Landschaftswasserhaushalt im Flusseinzugsgebiet derElbe, Forschungszentrum Jülich, Reihe Umwelt Volume 12, 1998.

[8] Übertragung und Weiterentwicklung eines robusten Indikationssystems für ökologischeVeränderungen in Auen (RIVA), Zwischenbericht für den Projektzeitraum 9/97 bis 5/99,Umweltforschungszentrum Leipizig-Halle GmbH, Leipzig, 1999.

[9] Ecological Research in the Elbe Catchment Area (Elbe Ecology), Research conception bythe German Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie(BMBF), Bonn, August, 1995.

[10] Ökologische Zusammenhänge zwischen Fischgemeinschafts- und Lebensraumstrukturender Elbe- ELbeFIsche (ELFI), BMBF Projekt 0339578, Sachstandsbericht 1.3.97-31.1.1999,Universität Hamburg, Zentrum für Meeeres- und Klimaforschung (ZMK), Insititut fürHydrobiologie und Fischereiwissenschaft Elbelabor, Februar 1999.

[11] Untersuchung der Auswirkung von Maßnahmen im Elbevorland auf dieStrömungssituation und die Flußmorphologie am Beispiel der Erosionsstrecke und derRückdeichungsbereiche zwischen Wittenberge und Lenzen, Zwischenbericht 1998,Bundesansalt für Wasserbau, Karlsruhe-Hamburg-Berlin.

[12] Horst Behrendt, Martin Bach, Peter Huber, Matthias Kornmilch, Dieter Opitz, Wolf-Gunther Pagenkopf, Oliver Schmoll, Gaby Scholz, Ulrike Schweikart & Roger Uebe,Nährstoffbilanzierung der Flussgebiete Deutschlands, Ergebnisse des Vorhabens FKZ 296 25515 im Rahmen des Umweltforschungsplans des BMU, Umweltbundesamt, Institut fürGewässerökologie und Binnenfischerei im Forschungsverbund Berlin e.V.

24

[13] Jürgen Schmidt, Michael von Werner und Anne Michael, EROSION 2D/3D EinComputermodell zur Simulation der Bodenerosion durch Wasser, Landesanstalt fürLandwirtschaft/Landesamt für Umwelt und Geologie, Freiberg, September, 1996.

[14] Elmar Fuchs, The floodplain model INFORM for predicting ecological impacts causedby alterations in mean river stage, Federal Institute of Hydrology, IAHS at IUGG,Birmingham, 1999.

[15] Indikationssystem für ökologische Veränderungen in Auen, Posterbeiträge TeilprojektV.1, Bundesanstalt für Gewässerkunde, Referat U21, Stand 15.10.1999, ForschungsverbundElbe-Ökologie, Koblenz-Berlin, 1999.

[16] Trejtnar, K. (1993): Vliv provozu na tocích a t]HQt� YRGRKRVSRGi VNêFK� soustav najakost vody (Einfluß des Verkehrs auf Flüsse und der Führung des wasserwirtschaftlichenSystems auf die Gewässergüte). Povodí Labe a.s. Hradec Králové, 132 S.

[17] Schöl, A., T. Bergfeld, V. Kirchesch u. D. Müller: IKSMS-Project: Oxygen budget andbiological processes in the regulated rivers Mosel and Saar, Final report 1997 - Bundesanstaltfür Gewässerkunde, BfG - 1091, Textband 78 S. + Annex-Band 167 S.

[18] Desortová, B. (1993): Distribuce fytoplanktonu podél toku Labe ve vztahu kekoncentraci åLYLQ� �9HUWHLOXQJ�des Phytoplanktons entlang des Laufs der Elbe im Bezug zurNährstoffkonzentration). VÚV T.G.M Praha, Dez. 1993, 16 + 4 S.

[19] Kersebaum, K. C. u. J. Richter (1991): Modelling nitrogen dynamics in a soil-plantsystem with a simple model for advisory purposes. Fertilizer Research 27, 273 - 281

[20] Kersebaum, K. C. (1995): Application of a simple management model to simulate waterand nitrogen dynamics. Ecological Modelling 81, 145 - 156.

[21] Kersebaum, K. C. (1999): Model based evaluation of land use and managementstrategies in a nitrate polluted drinking water catchment in North-Germany. In: R. Lal (ed.):Integrated Watershed Management in the Global Environment. CRC Press, Boca Raton. 223-238.

[22] Kersebaum, K. C. u. K.-O. Wenkel (1998): Modelling water and nitrogen dynamics atthree different spatial scales - influence of different data aggregation levels on simulationresults. Nutrient Cycling in Agroecosystems 50, 313-319.

[23] Kunkel, R. & Wendland, F. (1998): Der Landschaftswasserhaushalt imFlußeinzugsgebiet der Elbe – Verfahren, Datengrundlagen und Bilanzgrößen. FZ Jülich,Buchreihe Umwelt 12, 107 S.

[24] Wendland, F. & Kunkel, R. (1999): Der Landschaftswasserhaushalt imFlußeinzugsgebiet der Elbe (Deutscher Teil) Hydrologie und Wasserbewirtschaftung 43, H.5,226 - 233.

[25] Kunkel, R. & Wendland, F. (1999): Das Weg-/Zeitverhalten der unterirdischenAbflußkomponente im Flußeinzugsgebiet der Elbe.- FZ Jülich, Buchreihe Umwelt Bd. 13,122 Seiten.

25

[26] Kunkel, R. & Wendland, F. (1997): WEKU – a GIS-supported stochastic model ofgroundwater residence times in upper aquifers for the supraregional groundwatermanagement.- Environmental Geology 30 (1/2), 1-9.

[27] Glugla und Fürtig, documentation on ABIMO Model, Bayerisches Landesamt fürWasserwirtschaft (Hrsg. 1998): Kontinuierlicher Abfluß und Stofftransport – IntegrierteModellierung unter Nutzung von Geoinformatioonssystemen. Neubiberg.

[28] Krysanova, V., D.-I. Müller-Wohlfeil and A. Becker (1996) Integrated Modelling ofHydrology and Water Quality in mesoscale watersheds. PIK Report No. 18, July 1996, PIK,Potsdam.

[29] Krysanova, V., D.I. Müller-Wohlfeil and A. Becker (1998) Development and test of aspatially distributed hydrological / water quality model for mesoscale watersheds. EcologicalModelling 106 (1-2), 261-289.

[30] Krysanova, V., Becker & Klöcking, B. (1998) The linkage between hydrologicalprocesses and sediment transport at the river basin scale. In W.Summer E.Klaghover,W.Zhang (eds.) Modelling Soil Erosion, Sediment Transport and Closely RelatedHydrological Processes. IAHS Publications no. 249, p. 13-20.

[31] Krysanova, V., and A. Becker (1999) Integrated Modelling of Hydrological Processesand Nutrient Dynamics at the River Basin Scale, Hydrobiologia, 410, 131-138.

[32] Krysanova, F. Wechsung, A. Becker, W. Poschenrieder and J. Gräfe (1999) Mesoscaleecohydrological modelling to analyse regional effects of climate change, EnvironmentalModelling and Assessment, 4, 259-271.

[33] Krysanova, V., Gerten, D., Klöcking, B., & Becker, A. (1999) Factors affecting nitrogenexport from diffuse sources: a modelling study in the Elbe basin. In: Impact of Land UseChange on Nutrient Loads from Diffuse Sources, Proceedings of Symposium in Birmingham19-30 July 1999, IAHS Publications no. 257, 201-212.

[34] F.Wechsung, V.Krysanova, M.Flechsig & S.Shaphoff (2000) May land use changereduce the water deficiency problem caused by reduced brown coal mining in the state ofBrandenburg? Landscape and Urban Planning (accepted).

[35] Diepenbrock, W., Rost, D. und Hülsbergen, K.-J. (Hrsg.) (1999): Informationssystem„Agrar-Umweltindikatoren“ und Bilanzierungsmodell „REPRO“. Forschungsbericht imAuftrag des Ministeriums für Raumordnung, Landwirtschaft und Umwelt des LandesSachsen-Anhalt. Martin Luther-Universität Halle-Wittenberg.

[36] Hülsbergen, K.-J., Diepenbrock, W. u. Rost, D. (1999): Konzept zur Analyse undBewertung von Umweltwirkungen im Landwirtschaftsbetrieb. Vortrag zum 111. VDLUFA-Kongreß vom 13-17. September 1999 in Halle/Saale. Erscheint in VDLUFA-Schriftenreihe.

[37] A. Hoogerwerf (ed.), Overheidbeleid, Samson Tjeenk Willink, 4th print (in Dutch), 1989.

[38] J.G.M. Rademakers and H.P. Wolfert, Het River-Ecotopen -Stelsel, Een indeling vanecologisch relevante ruimelijke eenheden ten behoeve van ontwerp- en beleidsstudies in het

26

buitendijkse rivierengebied, RIZA, Rijksinstituut voor Integraal Zoetwaterbeheer enAfvalwaterbehandeling, Lelystad, The Netherlands, 1994.

[39] IKSE, Erster Bericht über die Erfüllung des „Aktiensprogramms Elbe, Magdeburg,Oktober, 1998.

[40] Norbert Busch, Wolfgang Froehlich, Rita Lammersen, Reinhard Oppermann und GerdSteinebach, Stroemungs- und Durchflussmodellierung in der Bundesanstalt fuerGewaesserkunde, Mathematische Modelle in der Gewaesserkunde, Stand und Perspektiven,Beitraege zum Kolloquiem am 15./16.11.1998 in Koblenz, Bundesanstalt fuerGewaesserkunde, Koblenz-Berlin, Mitteillung Nr. 19, Koblenz, August 1999, 70-82.

[41] Volker Kirchesch, Regina Eidner und Dieter Mueller, Gewaesserguetemodellerung in derBundesanstalt fuer Gewaesserkunde, Mathematische Modelle in der Gewaesserkunde, Standund Perspektiven, Beitraege zum Kolloquiem am 15./16.11.1998 in Koblenz, Bundesanstaltfuer Gewaesserkunde, Koblenz-Berlin, Mitteillung Nr. 19, Koblenz, August 1999, 105-114.

[42] Peter Krahe, Karlheinz Daamen, Gerhard Glugla, Rainer Muelders, Katharina Richterund Klaus Wilke, Mathematische Modelle in der Gewaesserkunde, Stand und Perspektiven,Beitraege zum Kolloquiem am 15./16.11.1998 in Koblenz, Bundesanstalt fuerGewaesserkunde, Koblenz-Berlin, Mitteillung Nr. 19, Koblenz, August 1999, 36-45.

[43] Michael Kinder, Elmar Fuchs, Helmut Giebel, Michael Schleuter und Franz Schoell,Oekologische ModellAnsaetze – Anwendungsbeispiele in der Bundesanstalt fuerGewaesserkunde, Mathematische Modelle in der Gewaesserkunde, Stand und Perspektiven,Beitraege zum Kolloquiem am 15./16.11.1998 in Koblenz, Bundesanstalt fuerGewaesserkunde, Koblenz-Berlin, Mitteillung Nr. 19, Koblenz, August 1999, 115-126.

[44] Peter Burek, Jürgen Ihringer, Langzeitlicher Modellierung der Wasserhaushaltsdynamikan der Elbe, Insitut für Wasserwirstschaft und Kulturtechnik der Universität Karlsruhe.

[45] Frank Ritzert, Franz Nestmann, Influence of silted groynefields on waterleveldevelopment instancing the Elbe river, Instituten of Water Resources Management, Hydraulicand Rural Engineering, University of Karlsruhe.

[46] Volker Kirchesch und Andreas Scholl, Das Gewässergütemodell QSIM - Ein Instrumentzur Simulation und Prognose des Stoffhaushalts und der Planktondynamik vonFliessgewässern, HW 43. 1999, H. 6, 302-309.

[47] S. Bergström, The HBV Model, in: Singh, V.P. (ed.), Computer models of watersheedhydrology, Water Resources Publications, Highlands Ranch, Colorado, 443-476, 1995.

[48] Ulf Mohrlok, Gerhard Jirka, Grundwasserdynamik in den Auen des Elbetals: Aspekte derDeichrückverlegung and der Oheremündung, Fachtagung Elbe, Dynamik und Interaktion vonFluss und Aue, 4. bis 7. Mai 1999, Wittenberge, Universität Karlsruhe, pp. 76-79.

[49] Peter Burek and Jürgen Ihringer, Langzeitliche Modellierung derWasserhaushaltsdynamik an der Elbe, IWW Universität Karlsruhe.

27

[50] J.W. Pulles, Beleidsanalyse voor de waterhuishouding in Nederland: PAWN,Hoofddirectie Rijkswaterstaat, Staatsuitgeverij, ‘s Gravenhage, 1985.

[51] Ökologische Forschung in der Stromlandschaft Elbe - eine Fördermassnahme desBundesministeriums für Bildung und Forschung (BMBF), Statusseminar Elbe-Ökologie,Tagungsband Nr. 6, BfG Mitteilung, Projektgruppe Elbe-Ökologie inder Bundesanstalt fürGewässerkunde, Koblenz - Berlin.

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7. FIGURES

Figure 1. Interaction of physical processes with natural and user functions.

naturalfunctions

userfunctions

physicalsystem

29

Figure 2. General system description for the Elbe based on completed and ongoing research in theElbe Ecology and Elbe 2000 programs. For clarity system interactions are not shown here.

economic:econ growth

(00)

demographic:pop growth

(02)

hydrology:precipitationdischarge

(03)

scenarios

water supply- drinking- irrigation- industry

(50)

economic:- income

- employment(51)

flooding safety- return period

(52)

ecological: -biodiversity- abundance

- hab condition- water quality

(53)

navigationcapacity

(54)

managementobjectives

morphology- river bank

- river bottom(40)

quantity/qualitycatchment

runoff(42)

pollutant:Czech input

(04)

abioticconditions

shipping(20)

agriculture(21)

residence(23)

industry(24)

fisheries(26)

groundwaterlevel(44)

social-economic functions

effluentreduction

(10)

sandsuppletion

(12)

retentionponds(13)

shiftingdikes(14)

fish relatedmeasures

(15)

measures

riverhydraulics

(41)

riverwater quality

(43)

floodplains(31)

river (32)

ecological functions

zoning(16)

gravelmining

(22)

tourismrecreation

(25)

river bank(30)

groinmodification

(11)

catchment(33)

groundwaterquality

(45)

30

Figure 3. Tentative structure for the prototype DSS for the Elbe with institutes having expertise inElbe Ecology program.

LAND USE

1D HYDROMORPHOLOGY MODULE

ECOLOGICAL MODULE(floodplain/river/bank)

groinmodification

dikeshifting

shipping

Catchment Module

social-economic

state

ecologicalstate

River Section Module

floodrisk

IGBPIK

FZ JülichBTU Cottbus

Uni Halle

BAW

Uni OSELBE 2000

IGBPIK

BAW Uni Karlsruhe

TU BraunschweigTU DarmstadtTU Dresden

Uni HamburgUFZ/RIVATU DresdenUni Halle

EXAMPLE ELBE-DSS

1D/2D HYDROMORPHOLOGY MODULE

River Module

HYDROLOGY

LAU/RWTH ACBAW

BAW

agricultureforestry

BTU CottbusUni Halle

1D RIVER WATER QUALITY+ TOXICANTS

UniOSELBE 2000ARGE Elbe

BAW

sand suppletion

flood frequency,discharge

UFZ/RIVA

BAWUni Karlsruhe

BAWLAU/IÖW

BfGBAW

TU Darmstadt

chemicalstate

urbanwastewaters

Point Diffuse

Uni OS

River Basin

31

Organic substances in water: concentrations andloads, directly before and beyond the confluenceto the Elbe

Heavy metals in sediment s (also remobilizationfrom different water quality levels)

Heavy metals in sediment s(also remobilization fromdifferent water quality levels)

Heavy metals in filtrates, suspended matter andsediments

Organics (DDT/Metabolite, Chlorbenzol &derivats , PCB-Kongenere, HCH-Isomere) insurface sediments

Polar organics (an- and nonionic tensides,phthalates, phosphoric acid ester, Triphenyl-phosphinoxid, aromatic sulfonic acids) fromwater samples

32 drinking water-relevant organics in watersamples 6 sample points in former DDR

Pesticides (N/P-Pesticides, polar pesticides,aliphatic chlorocarbonacids, Nitrofen: nearly 70substances) in filtrate

Heavy metals in filtrate,suspended matter and sediment(also remobilization fromdifferent water quality levels)

Organic substances in water (relevant todrinking water): concentrations and loads,(before and beyond the confluence to the Elbe)

Polycyclic aromatic carbons: concentrations insediment and in water; abundance of PAK-degradating bacteria

Heavy metals in sediment (also remobilizationfrom different water quality levels)

Pollutants in the Saale reservoir

Heavy metals in free water,suspended matter and sediment(also remobilization fromdifferent water quality levels)

Figure 4a. Data from the Elbe 2000 research program.

32

Figure 4b. Data from the ARGE measuring program.

Sampling stations B:General parameters, sumparameters, nutrients, an- andcations, EDTA/NTA, heavy metals,chlor Chlorohydrocarbons, PAK,BTXE, Organozinn, (biologicalparameters), PBSM, radiologicalparameters, scents (Duftstoffe)

Sampling stations A:General quality parameters, sumparameters, nutrients, an- undcations, Komplexbildner, heavymetals, Chlorohydrocarbons, PAK,BTXE, Organozinn, biologiclparameters, PBSM, radiologicparameters, scents (Duftstoffe)

Sampling stations A = at this stations allspecified parameters were measured

Sampling stations B = at this stations not allparameters were measured (irregularmeasured parameters in brackets)

1 Cuxhaven2 Brunsbüttel3 Grauerort4 Schulau5 Blankenese6 Seemannshöft7 Altenwerder8 Bunthaus9 Zollenspieker10 Geesthacht11 Lauenburg12 Boizenburg

14 Schnackenburg15 Cumlosen16 Wahrenberg17 Tangermünde18 Hohenwarthe19 Magdeburg20 Breitenhagen21 Dessau22 Roßlau23 Wittenberg24 Dommitzsch25 Zehren

Sampling stations from north to south

33

Figure 4c. The Elbe catchment with KM indication.

proposedstudy area

Figure 5. Simplified pilot design for a DSS for the Elbe catchment.

precipitation(mm/month)

river discharge(m3/s)

water/bed level(m)

navigability(ton/yr)

water quality(indicators)

flood risk (DM)

ecologicalquality

(indicators)

groundwaterlevel (m)

flooding frequency(days/yr)

inundation depth

(m)

elevation(m)

catchmentland use

floodplain ecology(speciespresence

abundance)

runoff(mm)

floodplainland use

a

a b

c

d

e

f

g

h

h

i

j

k

l

m

n

o

p

q

managementobjective

managementmeasure

groinmodification

dikeshifting

effluenttreatment

dikeshifting

effluenttreatment

zoning

zoning

r

s

35

8. TABLES

Table 1research institute/sub project

maincontact person

processes/researchthemes(MODELNAME)

model type key inputvariables /datarequirements

keyoutputvariables

x∆ t∆ L T dev’tstage

stud

y ar

ea(K

M E

lbe)

appl

icat

io

RIVA-UFZ Halle dr. Robert Böhnkeboehnke@hdg.ufz.de

groundwater flowfinite elt method

2D numeric flood leveltransmissivity

groundwaterhead

1m-

10 m

1-100days

2x3km

5-30yrs

compl 283-285.5241.8-243.6

if data

IfW- TU Darmstadt Hector Montenegrohector.montenegro@hrzpub.tu-darmstadt.de

groundwater flowinteraction withsurface runoffH0150

2D, quasi 3Ddynamic

porosivitytransmissivity

GWL >30m

1day

1000ha

30 y

r

1994-

1999

476-

485

gene

ric

RIVA-BfG Koblenz dr. Elmar Fuchsfuchs@bafg.de

floodplainhabitat

1Dstatisticalcorresp analysis

groundwater levelgroundwater qualityvarious habitatconditions

speciesabundance

1 m

stea

dy s

tate

3-6km

stea

dy s

tate

compldec200

283-285242-243417-418

RIVA-BfG Koblenz Marcus Rinkrink@bafg.de

multivariatestatisticalevaluationmethodsecological models

nD / 1D-3Dordinationhabitat/biotopepreference modelsGLM

biotic data:vegetation, molluscscarabide beetlesabiotic data:physico-chemical soiland hydrologicalvariables

response ofindicatorspeciesstableexplanatoryvariables

steady state statisticalmodels, later on implemented

in the prognostic modeldescribed by E. Fuchs

213-285242-243417-418

UFZ Leipzig-Halle Prof. Krönertkroenert@bdf.ufz.de

water balancediffuse sources

hydrologicalagriculturalREPROABIMOCANDY

P, N 3D0-10m

100-1000

m1-10km,

PastleLöss

region?

36

Uni Hamburg dr. Ralf Thielrthiel@uni-hamburg.de

fish-habitat model 2Dsteady state,statistical multivariate ordination,regression, GIS

water depthstream velocitysubstrateflooding duration/freqrelated parameters

abundancespecieslife strategies

1-100m

stea

dy s

tate

6 km

stea

dy s

tate

? 418-425 325-489

TU Berlin Jürgen Meyerhoffmeyerhoff@imup.tu-berlin.de

willingnesstopayfor ecologicalimprovement

description ofpossibleimprovements

money per ha complElbe

complElbe

Uni Karlsruhe Bruno Büchelebruno.buechele@bau-verm.uni-karlsruhe.de

hydrologymorphodynamics

1D-2D dischargeprecipitation

water levelsedtransportvelocitydischarge

Elbe

Uni Karlsruhe Dr. U. Morlok groundwater flow analytical2D

groundwaterlevelinfiltration

ground waterlevel

3-400m

1-7days

50km2

3or

30 yrs

compl KM340-350

genElbecalib

needed

Uni Karlsruhe M. Helms flood level regression1Dnon-dynamic

discharge flood level 10-100m

- devt KM0-536

Uni Karlsruhe M. Helms hydrology statistical precipitationBAW Karlsruhe Petra Faulhaber

petra.faulhaber@baw.dehydraulicsnavigationdikes/groynesbank/bed erosion

1D-2Dscale models,numerical

dischargeriver geometrygrain sizeh(Q,x)

water levelbed level

2-100m

min-days

20-

80 km

60 d-

30 yr

init-

compl

KM120-236438-495

?

TU Darmstadt Martin Krebs hydraulics onecologyshipping

2D groyne shapedischargemorphology

flow velocitywater depth

ongo

ing

LAGS Rühstadt Jochen Purps floodplain forests

RWTH Aachen Dirk Schwanenbergschwanenberg@iww.rwth-aachen.de

2Dinstat nonlin

water levelvelocityterrain model

flood levelprediction

10-

50 m

0.5-

10 s

2x10km

+ flpl

ca1

week

compl&

valid

KM341-351411-422

?

37

NNA Schneverdingen(no questionnaire)

dr. J. Prüter Leitbilder; criteria forecological qualityof soil, water andorganisms

land use scenariosGIS

? ? ? ? ? ? 1997-

2000

low

er M

itte

lElb

e-N

iede

rung

(nea

r H

ambu

rg)

?

TU Jena(no questionnaire)

dr. R. Haupt impact ofagricultureon runoff andfloodplainecology

groundw dynamicsbiomonitoringcost-benefit

agricultural practice N,P loadbiodiversity

? ?

ca. 2

8000

ha

? 1996-1999

UN

stru

tt

NLÖ Hildesheim M. Kämmereit impact of sluiceon fish migration

telemetric approach KM570

-590

TU Dresden dr. Robert Schwarzerobert.schwarze@mailbox.tu-dresden.de

nutrientrunoff

quasi-3D soil propert.land useN,C load

humidityN, C load

1-100km2

0.01-1

day

10-70km2

10-50yr

comp Chemnitz

Zwickauca. KM

100

catchment

BfG Berlin dr. Glugla rainfall- runoff(ABIMO)

2Dstochastic

precipitationevaporationland usesoil type

runoff depdata

1 yr

50 x

100

km 30

yrcompl near

KM 150Elbe

TU Freiberg Prof. dr. Schmidt soil erosion(EROSION2D/3D)

2D reliefvegetationsoil prop

erosion 10-100m

10min

idem event compl nearKM 50

Elbe

Uni München Prof. Kleeberg runofftransport (ASGi)

3D soil typeland useprecipitationtemperature

runoff 100-

1000m

day idem event compl nearKM 150

Elbe

38

BTU Cottbus dr. BeblikAJB@Hydrologie.tu-cottbus.de

agroecosystemmodelgeneral platform:meso-M(MINERVA)

land usesoil propcrop

runoffof water andN

25m

0.01-1

day

up to1000km2

1 day-30yr

compl nearKM 50

catchm

TU DresdenIfM

dr. Schwarzerobert.schwarze@mailbox.tu-dresden.de

N turnover(AkWa-M)groundwater(SLOWCOMP)

surface runoffcascade modelquasi-3D

precipitationtemperatureair humidity

runoffof water andN

25m

0.01-1

day

up to1000km2

1day

-30 yr

compl nearKM 50

catchm

IGB Berlin dr. Behrendt land use onN,P, sed load(MONERIS)

2Dretention fctspreadsheet model

land useprecipitation

N,P load 30km

5yr

catchment

25yr

compl catch -

ZALF Müncheberg dr. Kersebaum nitrogen dynamicsin soil-plantsystem (HERMES)

1D spatialdynamicmodel

groundwaterlvelprecipitationsoil typepracticefertilizationclimate data

nitrate loadN

depd

enin

g on

data

;var

iabl

e

0.5

– 1

.0 d

ays

Bu

nd

esla

ndb

ord

er

<=13 yr

complbut

underdev’t

nearBerlin,TorgauDrsdenKM75KM150

regi

onal

gene

rili

zabi

lity

BfG Berlin Werner Sauer sedimenttransport

1D shear stress,stream velocitygrain sizesed concentr

N balanceagroindiccostbenefit

1day-yrs

5km

compl 2.6-536

Uni Halle dr. Hülsbergen agriculture onrunoff (REPRO)cost-benefit

2Dstatistics

agriculturalpractice

? 0.1-200ha

yr - - compl catchm -

UFZ Leipzig-Halle dr. Frankoufranko@bdf.ufz.de

CN dynamicswater dynamics( CANDY )

scenariosspatialdynamic

soil parameteragrotechnicalmeteo

? days ? 1981-

1996

devt Parthearea

?

BfG Berlin dr. Anlauf impact of groyneon ecology(macroz, fish,vegetation)

exp sitesscale models(coop BAWdr Henschel)

groyne shape,length & angleshallownesshabitat structurevegetation

biomassbeetlesmacrozoobthsfish abund.biotope struct

10-

20 m

14 d

ays

-6

mon

ths

7km

5-10yrs

2003

KM

450

(Wit

tenb

erge

) MittelElbe

Uni Tübingen dr. Liedl surface runoff 1Ddiff equations

soil typeprecipitatongroundw levelhumidity

humidity var var 1km2

var devt KM265

general

applic

39

TU DresdenIfAM

dr. Schmitz groundwaterflow

analtyical solLaplace eqnxz 2D

groundw levelflood levelin/exfiltration

groundwleveldynamics

var var 1km2

var devt KM265

depndmodelcondit

ionsTU DresdenIfFF

dr Bonnbonn@forst.tu-dresden.de

growth scenariofor treesand water balance

growth model growthparameterstree typelight, heighthumidity

decisionmodel

- ? 1ha

1 -10yr

2001 KM264KM255

onlylocal

validity

TU DresdenIfWF

prof. dr. S. Wagner ecology offloodplain treesreforestation

growth modelnonlinear regression

growthparameterstree typelight, heightwood price etc

decisionmodel

- 1yr

fewkm

3-10yr

2001 KM264

onlylocal

validity

TU Braunschweig Christine Vogel beetlepop dynamics

2D GIScell autom.

elevationtemperaturelife parameters

suitability ? ? ? ? 2001 Dornwerdebei

Sandau

?

dr. Krysanova soil and waterintegrated model(SWIM)

2.5 D(max 10 soil layers)CN numberUSLE

reliefprecipitationevapotranspiration

runoffN/P leachingsed runoff

30-

200 m

100-

1000

km2

1 day 1-

50 yr

compl 10catchm

catchm

dr. Klöckingkloecking@pik-potsdam.de

hydrologicalcatchm model(ARC ECMO)

quasi 2Dland use scenarioshydr balance

soil typeland useclimate data

land usesurfacerunoff

variable

10-

105

km2

1 min-

1 yr

upto

100 yr

compl ? catchm

PIK Potsdam

dr. U. Haberlandt N, P leachinginto the soil(FUZNIL)

1D fuzzy basedN leachingat meso/macro scale

soil/cropfertilizerevapotrans

N Pload oversoil column

>1 km

mon

ths

105

km2

10-

100yr

init catchm catchm

FZ Jülich dr. Wendland hydrology +landuse on runoff(WEKU)

2D hydrologyland use

runoff 100 m-

1000m

n.a. ? compl catchm catchm

40

groundwater (GROWA98)

2Dretention times

hydrogeologicaldata

groundwaterflow

100-

1000m

>=1 yr

30yr

compl120

subcatchm

catchm

Uni Bremen IfOE dr. Hildebrandt habitat modelbeetles/birds

2D vegetationabiotic param

habitattype

270km2

- - 2 yr oct2000

KM474-568

ARUM Hannover dr. Horlitz Leitbilder qualitative Leitbild idem - - n.a. 1999 -Uni Lüneburg Bernd Redecker environmental

criteriadecision matrix

indicators idem - - n.a. 1999-

2000

-

BfG Peter Fischer, ReginaEidner, Maria Kalinová,Dieter Müller, JakubLanghammer

QSIMhydraulicswater qualityfor Czech Elbe

1Dquantitative

algae inputhydraulics

algae inputto Germansection

KM0-260(Czechpart)

ELBE 2000 dr. Prange, GKSS water quality: river water qualitydata samples

sedimentheavy metalsP, N, C pHvarious

waterquality

1992-

1995

KM -370(CS)

-KM+757

Insitute of EnvironmentalSystems ResearchUniv. Osnabrück

prof. dr. M. Matthiesdr. Jürgen Berlekamp

water quality:dischargewastewatertreatmenttransportGREAT-ER

data samples1D steady stateregressionMonte Carlo(N = 1000 cf 15min)point sources

river networkdischarge pointssedimentheavy metalsP, N, C pHvarious

waterquality;water flows

1-100;1-

5000;1-100;100-2000

yr

1 –

100

0 km

2 (a

rea)

2 –

10

km (

river

)

no

t-ap

plic

able

1996-1999

KM -370(CS)

-KM+757

various

applic.UK/

Germany

Table 2a. Functional requirements for Module 1submodule system variables institutes accuracy

input:• precipitation• landuse type map• relief map• vegetation map• landuse practice map

Hydrology

output:• N, P load• load by river section

PIKIGBUni OsnabrückFZ Jülich

51 −=∆x km.1=∆t mnth -1 yr.

input:• landuse type scenario• landuse practice scenario

Land Use

output:• economic valuation

PIKIGBUni Osnabrück

t

x

∆∆

to be decided

Table 2b. Functional description of Module 2submodule system variables institutes accuracy

input:• load by river section• hydraulics• river geometry by section

Water Quality

output:• river water quality (chemical,

ecological state)

PIKIGBUni OsnabrückElbe 2000

1D model5000=∆x m.1=∆t month

input:• flooding frequency• land value• water level• bed geometry• velocity

Social-EconomicValuation

output:• flood risk• navigatability (days)

BAWUni Karlsruhe

t

x

∆∆

to be decided

input:• precipitation• discharge• hydraulics• river geometry• sediment properties

Hydromorphology

output:• water level• flooding frequency• bed level• velocity

BAWUni Karlsruhe

1D model

t

x

∆∆

to be decided

42

Table 2c. Functional description of Module 3submodule system variables institutes accuracy

input:• discharge• river geometry• groyne shape and location• dike location• sediment inflow

Hydromorphology

output:• bed level• inundation depth• flooding frequency• water quality (from Module

2)

BAWUni Karlsruhe

2D model

t

x

∆∆

to be decided

input:• flow velocity• inundation depth• flooding frequency• bed level• water quality

Ecology

output:• habitat suitability map• species presence• species abundance• species diversity

UFZ/RIVATU Braunschw.TU DarmstadtUni HamburgUni Halle

2D model

t

x

∆∆

to be decided

43

Tor

gau-

Witt

embe

rg

MIt

tlere

Elb

e

Stec

kby-

Lödd

eritz

erF

rost

Ohr

e

Unt

er

Ha

Ve

l

Witt

embe

rgE

Elb

e

othe

r

problem /measure

involvedinstitutes

120-240

242-243

283-285

340-350

417-428

438-495

0-600

navigability BAW KM150

KM340

> KM350

bed erosion /suppletion

BAW * 210-220

bed erosion /groyneimprovement

BAW/ BfG

203-205235-236440-450

natureprotection

BfG * * * 222-302

flooding / dikedisplacement

BAW * 39 loc.176-555

water quality Uni Osn. *

Table 3. Management problems and planned/implemented measures for the Elbe.

44

Area

Tor

gau-

Witt

embe

rgM

Ittle

re E

lbe

Stec

kby-

Lödd

eritz

erF

rost

Unt

er

Ha

Ve

l

Witt

embe

rgE

Elb

e

regarding Institute 120-240

242-243

283-285

340-350

417-418

438-495

0-600

AVAILABLE DATAdike displac. BAW/LAGS *dike displac. IWW *groyne modif. BfG ca. KM 440groyne modif. TU Darmstadt *groundwater head BfG * * *groundwater head IWW *porosity IWW *Q-H IWW *Q-H BAW * *water level IWW * (9 Pegel)bed level IWW *river geometry IWW *river geometry BAW * *average flowvelocity

IWW *

daily discharge IWW * (9 Pegel)daily precip. IWW * ( 1450

stations )land use IWW *ecology BfG * * *bioch. waterquality param.

Elbe2000 * (20locations)

OPERATIONAL MODELSsed transport(HFBM)

BfG * (25sections)

KWERT BfG calibrated for KM 0-100 but extendable in principleQSIM BfG extendable in principleHBV BfG extendable in principleDigital TerrainModel

IWW *

Digital TerrainModel

BfG * * *

groundwater(MODFLOW)

BfG * *

Table 4. Locations of some operational models and available data. The “Pegel” arefound approximately at KM 50, 130, 210, 20, 300, 340, 400, 450 and 500.

45

index process 1D pilot study models(available at)

extended DSS models(2D/3D)

a rainfall-runoff HBV (BfG) statistical (IWW)b (non)-point sources QSIM (BfG) MONERISc runoff HBV GREAT-ERd water quality QSIMe hydromorphology KWERT (BfG)

applied Elbe KM 0 -100

Sobek (BfG), WAQUA(BFG)

f discharge - flooding KWERT Sobekg water level -

inundationDigital Terrain Model(BfG)

h elevation -inundation

DTM

i groundwater -ecology

INFORM (BfG) 2D model (RIVM)

j flooding - ecology INFORMk resistance KWERTl water depth -

shippingPAWN norms (WL |DH)

m land use - waterquality

QSIM

n inundation depth - flood damage

damage functions(Landesamt fürWasserwirtschaft)

o land use - flooddamage

damage functions

p flooding probability -q ecological quality RIVA norms (BfG)r vegetation -

resistanceKWERT (BfG) Sobek (BfG)

s groundwaterdynamics

Model Burek (IWW) Visual MODFLOW (BfG)

Table 5. Simple 1D models and data for the pilot study and models for the extendeddesign.